Au sujet de ce document
- sl:arxiv_author :
- sl:arxiv_firstAuthor : Rodrigo Nogueira
- sl:arxiv_num : 1904.08375
- sl:arxiv_published : 2019-04-17T17:20:14Z
- sl:arxiv_summary : One technique to improve the retrieval effectiveness of a search engine is to
expand documents with terms that are related or representative of the
documents' content.From the perspective of a question answering system, this
might comprise questions the document can potentially answer. Following this
observation, we propose a simple method that predicts which queries will be
issued for a given document and then expands it with those predictions with a
vanilla sequence-to-sequence model, trained using datasets consisting of pairs
of query and relevant documents. By combining our method with a
highly-effective re-ranking component, we achieve the state of the art in two
retrieval tasks. In a latency-critical regime, retrieval results alone (without
re-ranking) approach the effectiveness of more computationally expensive neural
re-rankers but are much faster.@en
- sl:arxiv_title : Document Expansion by Query Prediction@en
- sl:arxiv_updated : 2019-09-25T00:40:54Z
- sl:bookmarkOf : https://arxiv.org/abs/1904.08375
- sl:creationDate : 2022-01-05
- sl:creationTime : 2022-01-05T09:29:00Z
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